It also allows flexibility in terms of population and solutions representation size and the evaluation module containing the objective function is interchangeable.
Results on the solvability and solutions representation for these BVPs are widely used as an instrument of investigating weakly nonlinear BVPs [[3]].
Using the solutions' representation (see (2.5) below), we can see that this principle is reduced to positivity of the Cauchy (fundamental) function of (1.8) and positivity of its nontrivial solution with nonnegative initial conditions.
All our solution representations contain activity lists as a substantial element.
After formulation of stabilization problem in Section 1, we discuss formulas of solution representations in Section 2.
The approach utilizes two types of solution representations, a combination of multiple neighborhoods, and objective value-based cut inequalities for improving the evaluation of candidate solutions.
The algorithm uses on-line learning to balance computational loads between a construction and an improvement method, both working on high level solution representations.
For those reasons, we used a relatively lightweight LocalSolver implementation based on the solution representations for the genetic algorithms as a surprisingly strong benchmark.
For, we can see from (3.9) that it is useful to find other solution representations which are equivalent to (3.10) and (3.11), but use instead of, if.
For this reason, we developed several extended solution representations and modified decoding mechanisms that can all be seen as generalizations of the established SSGS approach for the RCPSP.
